model

The model is a formal representation of system. The model is describe in terms of components. There are 4 component classes:

  • genes which are mandatory

  • genes which are accessory

  • genes which are neutral

  • genes which are forbiden

Each genes can have Exchangeable. An exchangeable is another gene which can paly the same role in the system. Usualy an analog or homolog. The models describe also distance constraints between genes:

  • inter_gene_max_space

  • loner

  • multi_loci

and quorum constraints

  • min_mandatory_genes_required

  • min_genes_required

and if a gene can be shared by several systems (several occurrences of the same model)

  • multisystem

model API reference

ModelBank

class macsypy.model.ModelBank[source]

Store all Models objects.

__contains__(model: Model) → bool[source]

Implement the membership test operator

Parameters:

model – the model to test

Returns:

True if the model is in the Model factory, False otherwise

__getitem__(fqn: str) → Model[source]
Parameters:

fqn – the fully qualified name of the model

Returns:

the model corresponding to the fqn.

Raises:

KeyError – if the model corresponding to the name does not exist

__init__() → None[source]
__iter__() → Iterator[source]
Returns:

an iterator object on the models contained in the bank

__len__() → int[source]
Returns:

the number of models stored in the bank

__weakref__

list of weak references to the object (if defined)

add_model(model: Model) → None[source]
Parameters:

model – the model to add

Raise:

KeyError if a model with the same name is already registered.

Model

class macsypy.model.Model(*args, **kwargs)[source]

Handles a macromolecular model.

Contains all its pre-defined characteristics expected to be fulfilled to predict a complete model:

  • component list (genes that are mandatory, accessory, neutral, forbidden)

  • quorum (number of genes)

  • genetic architecture

__eq__(other: Model) → bool[source]
Parameters:

other – the other model to compare

Returns:

True if this fully qualified name is equal to other fully qualified name. False otherwise.

__gt__(other: Model) → bool[source]
Parameters:

other – the other model to compare

Returns:

True if this fully qualified name is greater than to other fully qualified name. False otherwise.

__hash__() → int[source]
Returns:

__init__(fqn: str, inter_gene_max_space: int, min_mandatory_genes_required: int | None = None, min_genes_required: int | None = None, max_nb_genes: int | None = None, multi_loci: bool = False) → None[source]
Parameters:

  • fqn – the fully qualified name of the model CRISPR-Cas/sub-typing/CAS-TypeIE

  • inter_gene_max_space – the maximum distance between two genes (co-localization parameter)

  • min_mandatory_genes_required – the quorum of mandatory genes to define this model

  • min_genes_required – the quorum of genes to define this model

  • max_nb_genes – The number of gene to be considered as full system Used to compute the wholeness. If None the mx_nb_genes = mandatory + accessory

  • multi_loci – if the systems can split in different loci on the genome

Raises:

ModelInconsistencyError – if an error is found in model logic. For instance genes_required > min_mandatory_genes_required

__lt__(other: Model) → bool[source]
Parameters:

other – the other model to compare

Returns:

True if this fully qualified name is lesser than to other fully qualified name. False otherwise.

__str__() → str[source]

Return str(self).

__weakref__

list of weak references to the object (if defined)

property family_name: str
Returns:

the family name of the model for instance ‘CRISPRCas’ or ‘TXSS’

filter(hits: list[CoreHit]) → list[ModelHit][source]

filter out the hits according to this model and cast them in ModelHit. The filtering is based on the name of CoreGene associated to hit and the name of ModelGene of the model (the name of the ModelGene is the name of the CoreGene embedded in the ModelGene) only the hits related to genes implied in the model are kept.

Parameters:

hits (list of macsypy.report.CoreHit object) – list of hits to filter

Returns:

list of hits

Return type:

list of macsypy.report.Model object

genes(exchangeable: bool = False) → set[ModelGene][source]
Parameters:

exchangeable – include exchangeable if True

Returns:

all the genes described in the model. with exchangeables if exchangeable is True. otherwise only “first level” genes.

get_gene(gene_name: str) → ModelGene[source]
Parameters:

gene_name – the name of the gene to get

Returns:

the gene corresponding to gene_name.

Raise:

KeyError the model does not contain any gene with name gene_name.

property inter_gene_max_space: int
Returns:

set the maximum distance allowed between 2 genes for this model

property max_nb_genes: int
Returns:

the maximum number of genes to assess the model presence.

property min_genes_required: int
Returns:

get the minimum number of genes to assess for the model presence.

Return type:

integer

property min_mandatory_genes_required: int
Returns:

get the quorum of mandatory genes required for this model

property multi_loci: bool
Returns:

True if the model is authorized to be inferred from multiple loci, False otherwise

property name: str
Returns:

the short name of this model